Applying complex systems theory and transition management to sustainable agriculture: a case study of Mount Wolfe Farm

Repercussions of conventional large-scale agriculture have been seen in the form of environmental damage and biodiversity loss. There are sustainable alternatives; however, due to the current agricultural system there are barriers to scaling them out. By utilizing aspects of transition management, these limitations can be identified and frameworks can be built to address them. This is all done in the hopes of transitioning the conventional agricultural system to one that is sustainable.

Feasibility of clustering road user trajectories in complex scenes for automatic identification of common traffic activities

Proactive road safety analysis allows for the pre-emptive diagnosis of road safety issues without direct observation of traffic accidents by observing accident precursor events instead (i.e. "traffic conflicts"). This approach to road safety diagnosis is made possible with the collection and analysis of large quantities of high-resolution road user trajectory data acquired from video data automatically.

Quantifying the value and risk of restoring wetland habitats in agricultural landscapes

Wetlands provide critical habitat and valuable ecosystem services. Land use conversion in Ontario, however, has led to substantial wetland loss. The restoration of wetlands on agricultural properties has the potential to offset wetland loss, yet these wetlands are also susceptible to contamination by pesticides.

Risk Margin for Claims and Premium Liability in Accordance with IFRS 17

The Building Block Approach (BBA) is one of the liability measurement approaches proposed in the new insurance contract standards - International Financial Reporting Standards (IFRS) 17. Of the three components under BBA, determining the risk margin is the most essential.

Data Poverty Project

In many developing countries, it is very difficult to survey vulnerable groups in a way that provides reliable research findings. These surveys also only give limited insights into the experiences of these populations. This project will develop a software program that can be used to model the probability of events occurring given the estimated probability of other events. These models can be developed with a small group of individuals and can give a better insight Into the network of events underlying the experiences of vulnerable groups.

Human kinematic optimal control learning and wearable inertial measurement unit alignment for rehabilitation

Physiotherapy is a type of rehabilitation that aims to restore a patient's quality of life after an injury, surgery, or stroke by improving their mobility. Through prescribed exercises and specialized equipment, physiotherapy helps the patient to regain their muscle strength, range of motion, and natural movement. Unfortunately, only rudimentary tools are available to the therapists for assessment and monitoring of patients. Our work focuses on developing wearable technologies that can help therapists with patient assessment and progress tracking.

Exploring Symbolic Techniques for Fast Robust Nonlinear Model Predictive Control of Autonomous Vehicles

The goal of this project is to design computationally-efficient solvers that can be used for autonomous vehicle control developments. Because autonomous cars have complex mathematical models, it is usually hard to perform their necessary control computations on-line and when the vehicle is running. Therefore, it is required to come up with much faster solvers for their controllers. At the end of this project, the developed control methods will be tested on an accurate simulation platform to evaluate their performance and robustness in realistic scenarios.

Quantifying Impact of Transportation Electrification on Electrical Power Grid and CO2 Emissions through Big Data Analysis of Vehicle Driving and Charging Profiles

In this project, charging and driving data of 1000 electric vehicles (EVs) across Canada will be monitored and analyzed to figure out the impact of EVs on the electrical power grid, and their potential capability to reduce CO2 emissions. For this purpose, the degree to which a particular electricity grid profile, the vehicle type and driving style, and charging patterns impact CO2 emissions will be studied.

End-of-line Testing for Safety and Quality with Machine Learning

Safety-critical systems are pervasive throughout our society with everyday objects such as airplanes, cars, trains, or medical devices. The requested functionality and expectations from these systems are growing rapidly and consequently, they become more complex. The complexity is usually handled by breaking the system into manageable smaller components and parts. Factories then integrate these parts into the final product. However, while some complexity can be managed
by this divide & conquer strategy, the assembly is still a challenging task.

Integration of planning and scheduling for an industrial-scale analytical services facility

The aim of this project is to develop a computer-based algorithm that will integrate a planning model with a scheduling model to improve operations management for analytical service facilities. An iterative decomposition algorithm that can provide optimal production scheduling sequences (in acceptable computational times) based on changes in the strategic planning decisions will be provided and tested on an actual industrial-scale facility. Integration of planning and scheduling studies for large-scale plant sizes like that considered in this study have not been reported in the literature.

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